This disclosure relates generally to search engines, and more particularly to an enhanced adaptive random tree framework to find segments of a population with different scorecard models containing locally optimized score weights.
Traditional manual techniques for segmenting a population, i.e. for dividing a population into pools of similar risks, for example, can involve a general process called “tree building.” Tree building relates to organizing each member of the population according to a criteria, where branches are formed of members having similar departures from the criteria. Members of the population can be accounts, transactions, persons, etc.
The traditional techniques make branch-by-branch decisions. For instance, these techniques start with an entire population of accounts, and analyzes all the potential “splitter” variables (e.g. income, number of trade lines, average balance, etc.) to first find the one splitter that results in the greatest increase in divergence, i.e. “predictive power”—for the whole population. The best splits of the resulting subpopulations are sequentially determined “locally” down through the growing tree, using only the information from each subpopulation to be split. The subpopulations are iteratively divided until the leaf nodes contain too little data, i.e. consuming data until none is left.
These techniques overlook valuable information embedded in other parts of the tree, however. Analysts must then spend significant time evaluating all possible splits for each of the cascading series of subpopulations in an effort to improve the predictive power of the system.